Paper Info
Title
Multi-Objective Optimization and Characterization of Pareto Points for Scalable Coding
Abstract
In this paper, we formulated the optimal bit-allocation problem for a scalable codec for images/videos as a graph-based constrained vector-valued optimization problem with many optimal solutions, which are referred to as Pareto points. Pareto points are generally derived using weighted sum scalarization; however, it has yet to be determined whether all Pareto points can be derived using this approach. This paper addresses this issue. When presented as a theorem, our results indicate that as long as the rate-distortion function of each resolution is strictly decreasing and convex and the Pareto points form a continuous curve, then all Pareto points can be derived using scalarization. The theorem is verified using the state-of-the-art scalable coding method H.264/SVC and a scalability extension of High Efficiency Video Coding (HEVC). We highlight a number of easily interpretable Pareto points that represent a good trade-off between candidate resolutions. The proximity point is defined as the Pareto point closest to the ideal performance for each resolution. We also model the Pareto points as a function of total bit rate and demonstrate that the Pareto points at other target bit rates can be predicted.
Year
DOI
Venue
2019
10.1109/tcsvt.2018.2851999
IEEE Transactions on Circuits and Systems for Video Technology
Keywords
Field
DocType
Encoding,Distortion,Codecs,Resource management,Rate-distortion,Optimization,Scalability
Mathematical optimization,Pattern recognition,Computer science,Regular polygon,Coding (social sciences),Multi-objective optimization,Artificial intelligence,Optimization problem,Codec,Pareto principle,Scalability,Encoding (memory)
Journal
Volume
Issue
ISSN
29
7
1051-8215
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Wen-Liang Hwang142958.03
Chia-Chen Lee200.34
Guan-Ju Peng3113.27